[05] When to Pull the Trigger on FIRE — Monte Carlo Says You're Already Free This is Part 5 of a 6-part series: Building Investment Systems with Python "You need 25x your annual expenses." That's the standard FIRE rule. For ¥9.6M annual expenses, that's ¥240M. Most people see that number and think: "I'll never get there." But the 25x rule assumes a fixed 4% withdrawal rate, zero income, zero ada
[04] The 90/10 Portfolio — Dividend Core + Growth Satellite with a Live Simulator This is Part 4 of a 6-part series: Building Investment Systems with Python In the manifesto, I described a 90/10 portfolio philosophy: 90% in dividend-growing core positions, 10% in a deep-value satellite aiming for 3-5x. Today we build both sides — the dividend snowball model for the core, and a live interactive s
comparando el análisis de secciones entre ELF y PE, explicando las diferencias de formato y cómo las abordaste. At last, Phase 3 for Binalyzer is now complete! It now lists sections for both PE and ELF files. I'll keep it short and sweet this time since most of the information can be already understood from reading my previous post, so I'll keep the key takeaways of what I did here. I thought you'
[03] Designing a Personal Commitment Line — Two Loans, One Defense System This is Part 3 of a 6-part series: Building Investment Systems with Python Every major corporation maintains a revolving credit facility — a pre-arranged borrowing line they can draw from instantly during a crisis. They pay a commitment fee for the privilege of having this standby capacity, even when they don't use it. The
[02] Stress Testing Your Life — What Happens at -30%, -50%, -60%? This is Part 2 of a 6-part series: Building Investment Systems with Python After the 2008 financial crisis, regulators required banks to run stress tests — hypothetical scenarios where markets crash 30%, 40%, 60% — and prove they could survive. Your personal balance sheet faces the same risks. If you hold a securities-backed loan,
Most AI prompts for developers are useless. Not because the AI is bad - because the prompt is vague. "Review my code" gets you generic feedback. "Act as a senior engineer" gets you a persona, not a result. The prompts below are from the Prompt Playbook, a collection built around one principle: tell the model exactly what to check, in what order, with what output format. That specificity is what se
Creando y desplegando una instancia en Amazon EC2 ¿Alguna vez te has preguntado cómo funcionan los servidores en la nube o cómo puedes publicar tu propia página web en internet sin necesidad de tener un servidor físico? En este laboratorio te guiaré paso a paso en el proceso de creación de una instancia en Amazon EC2, explicando de manera clara cada una de las configuraciones necesarias para que p
We are living in the golden age of developer productivity. With tools like Copilot and ChatGPT, you can generate hundreds of lines of boilerplate and complex API endpoints in seconds. It feels like magic. But there is a hidden danger lurking behind that flashing cursor: If you don't possess foundational architectural knowledge, AI will just help you build a Big Ball of Mud faster than ever before.